Identifying Galaxy Mergers in Simulated CEERS NIRCam Images Using Random Forests


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Abstract

jats:titleAbstract</jats:title> jats:pIdentifying merging galaxies is an important—but difficult—step in galaxy evolution studies. We present random forest (RF) classifications of galaxy mergers from simulated JWST images based on various standard morphological parameters. We describe (a) constructing the simulated images from IllustrisTNG and the Santa Cruz SAM and modifying them to mimic future CEERS observations and nearly noiseless observations, (b) measuring morphological parameters from these images, and (c) constructing and training the RFs using the merger history information for the simulated galaxies available from IllustrisTNG. The RFs correctly classify ∼60% of non-merging and merging galaxies across 0.5 < jats:italicz</jats:italic> < 4.0. Rest-frame asymmetry parameters appear more important for lower-redshift merger classifications, while rest-frame bulge and clump parameters appear more important for higher-redshift classifications. Adjusting the classification probability threshold does not improve the performance of the forests. Finally, the shape and slope of the resulting merger fraction and merger rate derived from the RF classifications match with theoretical Illustris predictions but are underestimated by a factor of ∼0.5.</jats:p>

Description
Keywords
310, Galaxies and Cosmology
Journal Title
Astrophysical Journal
Conference Name
Journal ISSN
0004-637X
1538-4357
Volume Title
Publisher
American Astronomical Society
Sponsorship
Space Telescope Science Institute (STScI) (JWST-ERS-01345.015-A)
Space Telescope Science Institute (STScI) (HST-AR-15802.001-A)